Question

我正在学习如何设置RNN-LSTM网络进行预测。我用一个变量创建了数据集。

与y(t) = 2.5x(t) + x(t-1) -0.9*x(t-2)的关系。我正在尝试设置RNN-LSTM来学习模式，但是它发生了我的程序错误。我的程序如下：

df= pd.read_excel('dataset.xlsx')

def split_dataset(data):
    # split into standard weeks
    train, test = data[:-328], data[-328:-6]
    # restructure into windows of weekly data
    train = np.array(np.split(train, len(train)/1))
    test = np.array(np.split(test, len(test)/1))
    return train, test

verbose, epochs, batch_size = 0, 20, 16
train, test = split_dataset(df.values)
train_x, train_y = train[:,:,0], train[:,:,1]


model = Sequential()
model.add(LSTM(200, return_sequences=True, input_shape = train_x.shape))
model.compile(loss='mse', optimizer='adam')

发生了ValueError：

ValueError: Error when checking input: expected lstm_35_input to have 3 dimensions, but got array with shape (8766, 1)

任何经验丰富的DS或pythoner都可以教我如何设置网络？

谢谢

Answer 1

对于基于LSTM的RNN，输入应为3维（批，时间，data_point）。我假设您的x变量的索引就是它的时间。在这种情况下，您必须将输入转换为某个窗口的批次，例如一个3的窗口，那么您的输入是：

批处理＃输入目标

0 x x [0：3] y [3]

1 x x [1：4] y [4]

2 x [2：5] y [5]

注意：因为您使用的是最后3个时间步长来预测下一个第4个值，所以y从t = 3开始。如果您已经说过，您的y已经从最近三个时间步开始计算了，那么y应该从0索引开始，即在批次0中，您将y [0]作为目标

更新，如下所示如果要具有多个序列，则可以将其建模为序列问题序列，并且将是N to M映射，您需要五个x值来预测三个y：

批处理＃输入目标

0 x x [0：5] y [3：6]

1 x x [1：6] y [4：7]

2 x [2：7] y [5：8]

Answer 2

当前，我已经创建了数据窗口，看起来就像我提到的案例一样。

下面是我的代码：

df= pd.read_excel('dataset.xlsx')

# split a univariate dataset into train/test sets
def split_dataset(data):
    train, test = data[:-328], data[-328:-6]
    return train, test

train, test  = split_dataset(df.values)

# scale train and test data to [-1, 1]
def scale(train, test):
    # fit scaler
    scaler = MinMaxScaler(feature_range=(0,1))
    scaler = scaler.fit(train)
    # transform train
    #train = train.reshape(train.shape[0], train.shape[1])
    train_scaled = scaler.transform(train)
    # transform test
    #test = test.reshape(test.shape[0], test.shape[1])
    test_scaled = scaler.transform(test)
    return scaler, train_scaled, test_scaled

scaler, train_scaled, test_scaled = scale(train, test)

def to_supervised(train, n_input, n_out=7):
    # flatten data
    data = train
    X, y = list(), list()
    in_start = 0
    # step over the entire history one time step at a time
    for _ in range(len(data)):
        # define the end of the input sequence
        in_end = in_start + n_input
        out_end = in_end + n_out
        # ensure we have enough data for this instance
        if out_end <= len(data):
            x_input = data[in_start:in_end, 0]
            x_input = x_input.reshape((len(x_input), 1))
            X.append(x_input)
            y.append(data[in_end:out_end, 0])
        # move along one time step
        in_start += 1
    return np.array(X), np.array(y)
train_x, train_y = to_supervised(train_scaled, n_input = 3, n_out = 1)
test_x, test_y =  to_supervised(test_scaled, n_input = 3, n_out = 1)

verbose, epochs, batch_size = 0, 20, 16
n_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]


model = Sequential()
model.add(LSTM(200, return_sequences= False, input_shape = (train_x.shape[1],train_x.shape[2])))
model.add(Dense(1))
model.compile(loss = 'mse', optimizer = 'adam')
history = model.fit(train_x, train_y, epochs=epochs, verbose=verbose, validation_data = (test_x, test_y))

但是，我对此还有其他疑问：

Q1：LSTM中的单位是什么意思？ [model.add（LSTM（units，...））]

（我为模型尝试了不同的单位，随着单位的增加，它将更加准确。）

Q2：我应该设置几层？

Q3：如何预测多步？例如基于[x（t），x（t-1））预测y（t），y（t + 1）我试图在n_out = 2函数中设置to_supervised，但是当我应用相同的方法时，它返回了错误

train_x, train_y = to_supervised(train_scaled, n_input = 3, n_out = 2)
test_x, test_y =  to_supervised(test_scaled, n_input = 3, n_out = 2)

verbose, epochs, batch_size = 0, 20, 16
n_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]

model = Sequential()
model.add(LSTM(200, return_sequences= False, input_shape = (train_x.shape[1],train_x.shape[2])))
model.add(Dense(1))
model.compile(loss = 'mse', optimizer = 'adam')
history = model.fit(train_x, train_y, epochs=epochs, verbose=verbose, validation_data = (test_x, test_y))

ValueError: Error when checking target: expected dense_27 to have shape (1,) but got array with shape (2,)

第3季度（续）：我应该在模型设置中添加或更改什么？

Q3（cont）：return_sequences是什么？我应该何时设置True？

如何建立LSTM网络？

2 个答案: